Improving Scheduling Techniques in Heterogeneous Systems with Dynamic, On-Line Optimisations

—Computational performance increasingly depends on parallelism, and many systems rely on heterogeneous resources such as GPUs and FPGAs to accelerate computationally intensive applications. However, implementations for such heterogeneous systems are often hand-crafted and optimised to one computation scenario, and it can be challenging to maintain high performance when application parameters change. In this paper, we demonstrate that machine learning can help to dynamically choose parameters for task scheduling and loadbalancing based on changing characteristics of the incoming workload. We use a financial option pricing application as a case study. We propose a simulation of processing financial tasks on a heterogeneous system with GPUs and FPGAs, and show how dynamic, on-line optimisations could improve such a system. We compare on-line and batch processing algorithms, and we also consider cases with no dynamic optimisations. Keywords-Scheduling, Heterogeneous System, Genetic Alg...

• Type: Journal
• Year: 2011
• Where: CISIS
• Authors: Marcin Bogdanski, Peter R. Lewis, Tobias Becker, Xin Yao
• Keywords: Application Parameters, CISIS 2011, Dynamic Optimisation, Genetic Algorithm, Software Engineering